o_control

k_min

which rule to use in approx() of the response before calculating the 
global mean, rule=1 means no extrapolation, rule=2 means to extrapolate the 
closest non-missing value, see <code><a rd-options="stats" href="/link/approx?package=FDboost&version=0.3-2&to=stats" data-mini-rdoc="stats::approx">approx</a></code>

rule

print error messages of model fit?

silent

defaults to FALSE, if TRUE cyclic splines are used

cyclic

arguments knots passed to <code><a rd-options="mgcv" href="/link/gam?package=FDboost&version=0.3-2&to=mgcv" data-mini-rdoc="mgcv::gam">gam</a></code>

knots

Function to control estimation of smooth offset

Regression models for functional data, i.e., scalar-on-function,
function-on-scalar and function-on-function regression models, are fitted
by a component-wise gradient boosting algorithm.

Sarah Brockhaus

FDboost

Boosting Functional Regression Models

David Ruegamer

Torsten Hothorn

with contributions by many others (see inst/CONTRIBUTIONS)

o_control function

which rule to use in approx() of the response before calculating the 
global mean, rule=1 means no extrapolation, rule=2 means to extrapolate the 
closest non-missing value, see <code><a rd-options='stats' href='approx'>approx</a></code>

arguments knots passed to <code><a rd-options='mgcv' href='gam'>gam</a></code>

o_control: Function to control estimation of smooth offset

Description

Usage

Arguments